In this lab, we will go through the plots created in Lectures 5 and 6, this time, using ggplot in R. The corresponding Altair code can be found within the lecture slide decks.

First and foremost let’s load the necessary packages. Obviously, we’ll need ggplot2 but as seen below, this is automatically loaded alongside the tidyverse library (which will be used data wrangling).

library(tidyverse)

Data sets

Stocks

stocks = read.csv(paste(path, "stocks.csv", sep=""))
stocks$date = as.Date(stocks$date)

Vega-lite Gapminder

gapminder <- read.csv("../data/vega-gapminder.csv")
# gapminder$year = as.Date(ISOdate(gapminder$year,1,1))
## (keeping dates as integers will be easier for calculation of the correlation matrix)

Vega-lite Wheat

wheat <- read.csv("../data/vega-wheat.csv")

Lecture 5

Direct labeling

Start with the default (with a change of theme to better match the example):

ggplot(stocks) + 
    aes(x = date,
        y = price,
        color = symbol) +
    geom_line() +
    ggthemes::scale_color_tableau()

remove the legend:

ggplot(stocks) + 
    aes(x = date,
        y = price,
        color = symbol) +
    geom_line() +
    ggthemes::scale_color_tableau() +
    theme(legend.position = 'none')

Here we use the same approach with geom_text and label as we did above. The difference is that we’re explicitly setting the data inside geom_text to use the dataframe that has been filtered to contain the max year only.

stock_order = stocks[stocks$date == max(stocks$date),]
stock_order = stock_order[order(stock_order$price),]

ggplot(stocks) + 
    aes(x = date,
        y = price,
        color = symbol,
        label = symbol) +
    geom_line() +
    geom_text(data = stock_order, vjust=-1) +
    ggthemes::scale_color_tableau() +
    theme(legend.position = 'none')

Axis titles

To demo these functions, we’ll use the diamonds data set

Axis formatting

The scales package helps with the formatting in ggplot.

Here we are using an alternative for heat map rectangles.

ggplot(diamonds) +
    aes(x = carat,
        y = price) +
    geom_hex()

Let’s change the units to $ on the Y axis

ggplot(diamonds) +
    aes(x = carat,
        y = price) +
    geom_hex() +
    scale_y_continuous(labels = scales::label_dollar())

Let’s change the y axis to scientific notation.

ggplot(diamonds) +
    aes(x = carat,
        y = price) +
    geom_hex() +
    scale_y_continuous(labels = scales::label_scientific())

Let’s change the y axis to SI units

# this broke... i'll need to find a working alternative
ggplot(diamonds) +
    aes(x = carat,
        y = price) +
    geom_hex() +
    scale_y_continuous(labels = scales::label_number_si())
## Warning: `label_number_si()` was deprecated in scales 1.2.0.
## ℹ Please use the `scale_cut` argument of `label_number()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

The scales package also helps us setting the number of ticks (breaks) on an axis.

ggplot(diamonds) +
    aes(x = carat,
        y = price) +
    geom_hex() +
    scale_y_continuous(
        labels = scales::label_dollar(),
        breaks = scales::pretty_breaks(n = 10)) +
    scale_fill_continuous(labels = scales::label_number_si())

You can remove an axis.

ggplot(diamonds) +
    aes(x = carat,
        y = price) +
    geom_hex() +
    theme(axis.title.x=element_blank(),
          axis.text.x=element_blank(),
          axis.ticks.x=element_blank())

Or set a theme that hides all axis objects.

ggplot(diamonds) +
    aes(x = carat,
        y = price) +
    geom_hex() +
    theme_void()

The classic theme is nice. There are many more sophisticated theme in the ggthemes.

ggplot(diamonds) +
    aes(x = carat,
        y = price) +
    geom_hex() +
    theme_classic()

Figure, axis, and legend titles

ggplot(diamonds) +
    aes(x = carat,
        y = price) +
    geom_hex() +
    labs(x = 'Carat', y = 'Price')

ggplot(diamonds) +
    aes(x = carat,
        y = price) +
    geom_hex() +
    labs(x = 'Carat', y = 'Price', fill = 'Number', title = 'Diamonds', subtitle='Small diamonds') +
    scale_y_continuous(labels = scales::label_dollar())

Lecture 6

Categorical Colours

The default categorical colormap in ggplot is not explicitly designed, but rather created by selecting equally spaced colors from the color wheel.

ggplot(iris) + 
    aes(x = Petal.Width,
        y = Petal.Length,
        color = Species) +
    geom_point(size = 5)

All useful color maps are not collecting in one place, but available through different functions and packages. For example, the color maps from color brewer are accessible via scale_color|fill_brewer|distiller (use the brewer suffix for categorical and distiller for sequential values).

ggplot(iris) + 
    aes(x = Petal.Width,
        y = Petal.Length,
        color = Species) +
    geom_point(size = 5) +
    scale_color_brewer(palette = 'Dark2')

All R colors maps can be viewed in this repo. The tableau colors used in Altair are accessible via the ggthemes package.

ggplot(iris) + 
    aes(x = Petal.Width,
        y = Petal.Length,
        color = Species) +
    geom_point(size = 5) +
    ggthemes::scale_color_tableau()

We could also set the colorscale manually via their HTML codes instead.

ggplot(iris) + 
    aes(x = Petal.Width,
        y = Petal.Length,
        color = Species) +
    geom_point(size = 5) +
    scale_color_manual(values = c('#FF7F50', '#4682B4', '#663399'))

Sequential Colour Schemes

The default color map for numerical values goes from dark to white, since the default background is dark.

ggplot(iris) + 
    aes(x = Petal.Width,
        y = Petal.Length,
        color = Petal.Width) +
    geom_point(size = 5)

It can be changed to the viridis (this is the same colour map that we looked at in lecture as a popular alternative in Altair) color map.

ggplot(iris) + 
    aes(x = Petal.Width,
        y = Petal.Length,
        color = Petal.Width) +
    geom_point(size = 5) +
    scale_color_viridis_c()

Reversing is possible via the same techniques as for axes, but it does not look great since the color legend is sorted “upside down”.

ggplot(iris) + 
    aes(x = Petal.Width,
        y = Petal.Length,
        color = Petal.Width) +
    geom_point(size = 5) +
    scale_color_viridis_c(trans = 'reverse')

There is a special syntax for colormaps that preserves the orientation of the legend while reversing.

ggplot(iris) + 
    aes(x = Petal.Width,
        y = Petal.Length,
        color = Petal.Width) +
    geom_point(size = 5) +
    scale_color_viridis_c(direction = -1)

Diverging Colour Schemes

Like in Altair, it is not that informative to use the default color map for diverging values. For demonstration purposes let’s look at the correlation matrix from the gapminder data set:

corr_mat = cor(gapminder[,-2])
library(reshape2)
## 
## Attaching package: 'reshape2'
## The following object is masked from 'package:tidyr':
## 
##     smiths
corr_df <- melt(corr_mat)

ggplot(corr_df) +
    aes(x = Var1,
        y = Var2,
        fill = value) +
    geom_tile()

The default bluered tableau diverging color map can be used via ggthemes.

ggplot(corr_df) +
    aes(x = Var1,
        y = Var2,
        fill = value) +
    geom_tile() +
    ggthemes::scale_fill_gradient2_tableau()

However, this sets blue as high values by default, which is against people’s intuition since red is often used for “hot” and blue or “cold”. We can either reverse the colormap, or use one from ColorBrewer instead

ggplot(corr_df) +
    aes(x = Var1,
        y = Var2,
        fill = value) +
    geom_tile() +
  scale_fill_distiller(palette = 'PuOr')

ggplot(corr_df) +
    aes(x = Var1,
        y = Var2,
        fill = value) +
    geom_tile() +
   scale_fill_distiller(palette = 'PuOr', limits = c(-1, 1))

Highlighting with colors and text labels

wheat = wheat[wheat$year > 1700,]

# Set the year to be highlighted to a separate value in a new column
wheat$'highlight' = FALSE
wheat[wheat$year == 1810, 'highlight'] = TRUE

ggplot(wheat) +
    aes(x = year,
        y = wheat,
        fill = highlight) +
    geom_bar(stat = 'identity', color = 'white') +
    ggthemes::scale_fill_tableau()

And remove the legend.

ggplot(wheat) +
    aes(x = year,
        y = wheat,
        fill = highlight) +
    geom_bar(stat = 'identity', color = 'white') + 
    ggthemes::scale_fill_tableau() +
    theme(legend.position = 'none')

To add annotations, we can use geom_text with the label aesthetic.

ggplot(wheat) +
    aes(x = year,
        y = wheat,
        fill = highlight,
        label = wheat) +
    geom_bar(stat = 'identity', color = 'white') + 
    geom_text(vjust=-0.3) +
    ggthemes::scale_fill_tableau() +
    theme(legend.position = 'none')

To get these to be the same colors as the bars, we can set the color aestethic, and add the corresponding color scale.

ggplot(wheat) +
    aes(x = year,
        y = wheat,
        fill = highlight,
        label = wheat,
        color = highlight) +
    geom_bar(stat = 'identity', color = 'white') + 
    geom_text(vjust=-0.3) +
    ggthemes::scale_fill_tableau() +
    ggthemes::scale_color_tableau() +
    theme(legend.position = 'none')

Now we can remove the gridlines.

ggplot(wheat) +
    aes(x = year,
        y = wheat,
        fill = highlight,
        label = wheat,
        color = highlight) +
    geom_bar(stat = 'identity', color = 'white') + 
    geom_text(vjust=-0.3) +
    ggthemes::scale_fill_tableau() +
    ggthemes::scale_color_tableau() +
    theme(legend.position = 'none',
          panel.grid.major = element_blank(),
          panel.grid.minor = element_blank())

If you want your label to represent the count (which we normally calculate in the geom for ggplot), you can set it to label = stat(count).

To set a specific annotation text, we could either use the same approach as in Altair of adding a new column to our data frame, or we could use the annotate function.

ggplot(wheat) +
    aes(x = year,
        y = wheat,
        fill = highlight) +
    geom_bar(stat = 'identity', color = 'white') + 
    annotate('text', label = 'The record year', x = 1800, y = 102) +
    ggthemes::scale_fill_tableau() +
    theme(legend.position = 'none')

Lecture 7

cars = read.csv(paste(path, "cars.csv", sep=""))
cars$Year = as.Date(cars$Year)

 
ggplot(cars) +
    aes(x = Year,
        y = Horsepower,
        color = Origin) +
    geom_point() +
    geom_line(stat = 'summary', fun = 'mean')
## Warning: Removed 6 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 6 rows containing missing values (`geom_point()`).

geom_smooth creates a loess trendline by default. The shaded gray area is the 95% confidence interval of the fitted line.

ggplot(cars) +
    aes(x = Year,
        y = Horsepower,
        color = Origin) +
    geom_point() +
    geom_smooth(method = 'loess', formula='y ~ x')
## Warning: Removed 6 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 6 rows containing missing values (`geom_point()`).

We can color the confidence interval the same as the lines.

ggplot(cars) +
    aes(x = Year,
        y = Horsepower,
        color = Origin,
        fill = Origin) +
    geom_point() +
    geom_smooth()
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Warning: Removed 6 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 6 rows containing missing values (`geom_point()`).

And also remove it.

ggplot(cars) +
    aes(x = Year,
        y = Horsepower,
        color = Origin,
        fill = Origin) +
    geom_point() +
    geom_smooth(se = FALSE, size = 2)
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Warning: Removed 6 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 6 rows containing missing values (`geom_point()`).

Similar to the bandwidth in Altair, you can set the span in geom_smooth to alter how sensitive the loess fit is to local variation.

ggplot(cars) +
    aes(x = Year,
        y = Horsepower,
        color = Origin,
        fill = Origin) +
    geom_point() +
    geom_smooth(se = FALSE, size = 2, span = 1)
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
## Warning: Removed 6 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 6 rows containing missing values (`geom_point()`).

If you wnat a linear regression instead of loess you can set the method to lm (linear model).

ggplot(cars) +
    aes(x = Year,
        y = Horsepower,
        color = Origin,
        fill = Origin) +
    geom_point() +
    geom_smooth(se = FALSE, size = 2, method = 'lm')
## `geom_smooth()` using formula = 'y ~ x'
## Warning: Removed 6 rows containing non-finite values (`stat_smooth()`).
## Warning: Removed 6 rows containing missing values (`geom_point()`).

Confidence intervals

In ggplot, we can create confidence bands via geom_ribbon. Previously we have passed specific statistic summary functions to the fun parameter, but here we will use fun.data because we need both the lower and upper bond of where to plot the ribbon. Whereas fun only allows functions that return a single value which decides where to draw the point on the y-axis (such as mean), fun.data allows functions to return three values (the min, middle, and max y-value). The mean_cl_boot function is especially helpful here, since it returns the upper and lower bound of the bootstrapped CI (and also the mean value, but that is not used by geom_ribbon).

You need the Hmisc package installed in order to use mean_cl_boot, if you don’t nothing will show up but you wont get an error, so it can be tricky to realize what is wrong.

ggplot(cars) +
    aes(x = Year,
        y = Horsepower,
        color = Origin,
        fill = Origin) +
    geom_ribbon(stat = 'summary', fun.data = mean_cl_boot, alpha=0.5, color = NA)
## Warning: Removed 6 rows containing non-finite values (`stat_summary()`).

    # `color = NA` removes the ymin/ymax lines and shows only the shaded filled area

We can add a line for the mean here as well.

ggplot(cars) +
    aes(x = Year,
        y = Horsepower,
        color = Origin,
        fill = Origin) +
    geom_line(stat = 'summary', fun = mean) +
    geom_ribbon(stat = 'summary', fun.data = mean_cl_boot, alpha=0.5, color = NA)
## Warning: Removed 6 rows containing non-finite values (`stat_summary()`).
## Removed 6 rows containing non-finite values (`stat_summary()`).

To plot the confidence interval around a single point, we can use geom_pointrange, which also plots the mean (so it uses all three values return from mean_cl_boot).

ggplot(cars) +
    aes(x = Horsepower,
        y = Origin) +
    geom_pointrange(stat = 'summary', fun.data = mean_cl_boot)
## Warning: Removed 6 rows containing non-finite values (`stat_summary()`).

And finally we can plot the observations in the backgound here.

ggplot(cars) +
    aes(x = Horsepower,
        y = Origin) +
    geom_point(shape = '|', color='grey', size=5) +
    geom_pointrange(stat = 'summary', fun.data = mean_cl_boot, size = 0.7)
## Warning: Removed 6 rows containing non-finite values (`stat_summary()`).
## Warning: Removed 6 rows containing missing values (`geom_point()`).